{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About\n",
    "This data set was generated using images from the [COCO 2017 Test Images (~41k images)](http://images.cocodataset.org/zips/test2017.zip) using the method described in [HomographyNet](https://arxiv.org/pdf/1606.03798.pdf).\n",
    "\n",
    "## Sample\n",
    "A sample consists of a pair of 128x128 patches and their corresponding corner offsets (4 pairs). Note that samples are not normalized in order to save disk space (33kB for 1 unnormalized sample vs 233kB)\n",
    "\n",
    "More specifically, a sample is a tuple of a (128, 128, 2) NumPy array (128x128 grayscale images stacked channel-wise) and a (8,) NumPy array (8-element vector containing the perturbations applied to the 4 corners).\n",
    "\n",
    "Each image is sampled 12 times on average generating a total of 485,376 samples in total.\n",
    "\n",
    "## Bundles\n",
    "For efficient loading, samples are bundled together in NumPy archives (.npz) but are uncompressed because the compression ratio is too low to make the additional CPU time worth it. Packing is done sequentially. That is, samples from a single image are grouped together. An archive is about 96 MiB in size and contains 3,072 samples.\n",
    "\n",
    "In this machine, a single archive can be read from disk (HDD) and loaded into memory in about 1.33s (first load, uncached). On succeeding loads (already cached), loading into memory takes about 200ms. This is much faster than having to load images individually on the fly (the HDD/CPU will bottleneck the GPU). With a batch size of 64, each archive is good for 48 iterations before having to load the next one (decreasing overall disk access and CPU wait time).\n",
    "\n",
    "## Downloading\n",
    "The files are hosted [here](http://192.168.1.126/~darwin/coco2017/). An archive's filename is also its MD5 hash, so you can easily check for file integrity.\n",
    "\n",
    "You can easily download all the archives using wget:\n",
    "\n",
    "`wget -rcnd -l 1 http://192.168.1.126/~darwin/coco2017/`\n",
    "\n",
    "There are 158 archives in all, or about 15GiB of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keys: ['images', 'offsets']\n",
      "images.shape: (3072, 128, 128, 2)\n",
      "offsets.shape: (3072, 8)\n",
      "sample images shape: (128, 128, 2)\n",
      "(64, 128, 128, 2)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "path = '/home/darwin/Projects/datasets/packed/f01906891b1f8ea2e2715d9f99f126f6.npz'\n",
    "\n",
    "archive = np.load(path)\n",
    "print('keys:', archive.files)\n",
    "\n",
    "images = archive['images']\n",
    "offsets = archive['offsets']\n",
    "\n",
    "print('images.shape:', images.shape)\n",
    "print('offsets.shape:', offsets.shape)\n",
    "\n",
    "sample_idx = 2335\n",
    "\n",
    "print('sample images shape:', images[sample_idx].shape)\n",
    "\n",
    "a=np.split(images,48)\n",
    "print(a[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa5b6b65400>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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TiZJUk7te7yI9zm21Wo34g9TrdVN6UyTTKEmN4GQ/2Ma+ffsWFfuQIIWEEkooREtCp0BF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\nUKBAgRSKRaFAgQIpFItCgQIFUvj/zMja8SGv5ucAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa5b6e7ff60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = np.split(images[sample_idx], 2, axis=-1)\n",
    "plt.imshow(img[0].squeeze(), cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa5b6b502b0>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m5ZD+mZrXgcjixRdfNDma8i/b+OlPfxozywGIRSCqk5HWruDYvD5F7/Maex07\nn12tVmNr2+l0YglkSeoqreiRaFRd5fUZAKwGxsTEhLXPudqzZ4/1m/oGH9eh7WsuhM9//vMAupGo\nXANfrWtyctL6oftPXcY5dupK+J26svuiwFejTXMoMNOtliMDehPVbDZNMaXZgr2Cr1qtGnRSaAZE\nPQnVB957HOpL64OlgiCI2K6BLrT08RDbtm2LxEuwXU9XMlvpIeE369DQkI2Z4dpDQ0N4/vnnAUQ3\nFn/nS8/pC0qam5uLeB8qzc7OmojA/mi9Dp8dmXOi969H6yl2c7mcveSas9Lvk1KpZKInr6lo4UOK\ngyCIZYVaXFy0MfNApJlwcHDQRAUW6r3xxhtN6a21I4CoEpIUhqGJGRRLhoeHTQzkfNHLtFqt2tpy\nPi5evGh91Hol/M7nxCwUCmnodEoppXR9tCmQAj0Z2+12rM6COpgQKdC8VSgUItWLgC4HIOTzKb6S\nQmOz2axx/CuJG/o7ntB85n333WcigpqXksKj1yON5EsyL/E7rVHAZyq8JmchB9OitV4UUoUjOdHS\n0lJMQau5FJOQ03qkzjec43w+b/A7aXxsl/c3Go2Y8pHKVv1OFaAcu47PF95VhybPXbU97rX33nvP\nrjM6NQxD22uMGiVSfPPNN21vqslTixjzmeTyvo/FYtHmjwrMhYUFa0MzNvO53HNaM+RaKUUKKaWU\nUoQ2BVKgeajVahkXIZfXfAOeK6kyx1dtAhBL+9Vut03OUy5IOZ33qTkyyXGG7bKvKysr5lxEuXZt\nbS2mlLuaSdKT6hR8puIwDI1jaOoyKlxZVFTlVD+WXC4XUyZqRmNyGVV4epMX51D7oUpaPz6tPakI\nwOtbdJzqgAV018kXk83lchbFqAl4gS539YlxVE+jJmOuLREZx/Lcc8/F0uVNTk6amZJIgclaJycn\nE3N3EF0yKW+z2bToVZ/JfMeOHYb8GB07NjZmY9d4kiSHLSCudN0IbYpDgVmHNBSag9PqxiT1Veei\nEcZ1Op1I0RUlhama4Zl25KRkFN7HAIh7zM3MzJidn1QoFGLPT/JVTzoUvFda0n1JHpBKPEwpYpw+\nfdo2ERWwW7duNdjJTa2byGvSk6wmGpuQVKnZK3b5Qvlxet8MtVawXT6zWCwmBpnxQGTYOJ+tPiNq\n2SED4UFerVZt3ngA8SVeWlqyPmk2Kyof+WIzBH15eTl2CCfF9lSrVRM56flIC9OOHTvs0OE9Wo5O\n94Sfv+sjkMFsAAAgAElEQVShVHxIKaWUIrQpkAJJkUJSwgy11QLR1FQ8ZRmCDfRECX7m8/kYGlAb\nuYoUXglFKpVKMTtxrVYz0x+5yOjoaAwZKHdbz1ddSSG0V0KqmU0Rg09/xqIwYRgaF9SswTSHcb7f\neuutmMemPtOHhifFHGj6NJ8uL4m2bNkSKSDjycNwTdun/SFCYOZrQu/+/n4bs8at8LdER9u2bTMu\nTYRApFqv1yPKUqC755566ikAvTn1OUOVFFWx3YmJiUi0KNBDBf39/fa37v31FNJK1xoZqZQihZRS\nSilCmwIp0Kmo0+kkJtkAohV61Afem6ny+XwsrZXe6zlzs9mMJfUMgiDxt0CXa3kT2fz8vMmIyiE8\np1VE4k/7KyUQSRoL21nvPp8hWBWIWuadHFHb9IllVOHp4wrUecnHWWitBJXpvYfiwMBALI/ClZy6\n1rtOT1a2qxmqvdmxr6/P5kjTpmmxWaCnm2k0GjGF9MrKSmwNdM19Zm3tG+dsdHTUlNSsm8HnlEql\nxIpWSY5gV8qt8KFFSf48KZvNYmBgAK1Wa13oCvQOgyTYpBpqn9dOc/AR8idtUk3Y4V2ONa02Nw5d\nW0ulksFf2pqr1eq6GvWrUdJ9SYFIJJ0HQmGOjyHR4+PjttlUGUZ3aL68SUrQJCUvYbJmbyIlbVq1\nxPi5PX/+fKzfusl93sYkt2h98bk+/P/s7Kzdp9YpWo/02VrJGei5kJfLZTtMqVS+8cYbY0pq3h+G\noR1KPBSUGbAkwB133GFrxMNBD94kd+4k8t8nHZobpVR8SCmllCK0KZAC07Gp4tCXwdLUa/o72tdV\nMcjv1Fce6CoGvf+/KhVJWvjDI5ZSqYRf+IVfAAD89m//NoCuCYz3kYOqIpCk6MRfU26sfSMlZeT1\n3EA9Dumzz7Jmr7zyinFBwt/h4WGbU/rzN5vNdSF8u92OlZ5L8sQk1ev1RMSXFPDlE+Po/R55JImB\n7LufD38PxQcVx2iW3bNnTyTBic5VpVIxjk6Pyf3799veIgrj77PZrKVqo+KzUCiYH8T9998PALj9\n9ttjHqQ6P0kiYpKCmbSR3J9XoxQppJRSShHaFEgB6J7c1WrVuALjCxQx8ITkadtqtWKKSTWb+SQa\nLHnP+4Aop1N0sF5W31KpZKmxqGe48847Yz7mSRzXy8GevFPUlZRGWrdAx85+0LuPStfJyUmLvtNS\naCRyv8nJyXXl1jAMY96CnU4nsfQ7++pNu5owVTmk94ZMMoMmccgrmTDJxZUYnbhlyxYzI7Nv77zz\nTqwwL/UCIyMj5uV45513Aug6FxGREZVqOLYPmc9ms4ZKaAoeHBxctz6IenPqnr4SatwIergapUgh\npZRSitCmQAph2K3PmM/nI3kRgN5JPT8/b6chkcLy8nJM96DabWqVqWPQZLBJJ2sSB/Lcpr+/37gk\nXVsHBwdjqCDJOpBkNlLZ2Z/yV3KHTtJLAPEUdORcd999tyEDIpylpSUbg6aw933TtHOeVKOe1H9e\n02SjXnZWxOeRXCaTibkXZ7PZ2Nipl9J21QGO3JW6As11oPuCyIp95FyNjIyYtYL7b+vWrYk1JjhX\n3MskWtmA3v5WlJQUI8P9T9MxEYaOUxGZb0uR8EZpUxwK7XYbKysrkUPBb+5MJhPbOOsp4jgxXETd\nrEkhvz7HYJKiUcOOuaFoyy6XyzGzWZLHoV907UeSYtLfo7/VQ0FfIB6EVHIRru7evdvMZ4S8b775\nppW706QlHsZqshIeHioO8O/1/DL0fh2PBlL5RDR6cCSVkEvy6qM50CeHqdfrtmcYI7N//37LUqW+\nCRQbeTgwl2J/f38kDB3o7kMfHp8kKmhhIa8M1Xnzey6bzZoPBddz//79iXEf3kckiTltlFLxIaWU\nUorQpkAKhH6XLl0yWMXTnty+WCzGCrCSuwHJvuaegylU8wlHgGjoKjkKFXBMcrGysmIQkJypUCjE\nuKNyfg+X/d+8X+djPUpCGxrrwRoNdFAihymVSpEivEAXilK8UM85712oMQ1J4/SKwCRzos53kvnR\noz/Nnp1UP4NtMKRYE694RaOarsm1b7/9duPynI8LFy7Yd1xjFgyen583cyOVlRqxmMS9Od+qhOaz\nOMfj4+OxeeOn1tQgqXNeksIxycPyaqnwYm1c090ppZTSf/H08ygwmwVwDMD5MAwfDYJgH4CvAxgB\n8DyA3wrD8IqZHkqlEg4fPoz5+Xk78Snn8ZTVE5OnsyYj5WepVDIZ2LvOJiUryefziS6ilJ35fHKO\noaGhmBJKIzPX89X31/x3SdFv2s56+gn9bmFhwXQEjI6komr37t04fPgwgKjsysg8ms8ymYxlpPay\nebPZvCInSoqVoGzOewYHBw19MVZBY168TqGvry9S/5FjJ5ckWhwYGLD2iCI0mSr7QSTVbDat0hMr\nbfX399s+07qiAPCRj3zEUBWRqiperxSHQgqCXlFbmkP379+/bvKgMOwlfaEuYmpqKpJbge2SkvRM\nV4pQTaKfh/jwLwGcAFC9/P9/B+Dfh2H49SAI/l8AXwLwR1dqgIEoR48eNT9xvwBzc3OmgeXBodl3\nODGNRiNmkVCRgYvHDZPP52N+/+122/rhQ2Gnp6fxzDPP2H0AcM899yQu0JW0vtfim54Ex5OsBIuL\ni+aXzwxA1KwvLS0ZRKdIUa/XbU41a7a3jesL6zd/kiJQD1yfc7Ner9vLq+1yvbkGWpaOlKSwoyJu\nZGTE1ujIkSMAevtkeXnZvBF5SD755JO4/fbbAfQOit27d+MjH/kIAOCll7qVEP/qr/4KAPC7v/u7\nFpugSr0ksYHXvPJP84FSTE56oZMsYhQ3ZmdnY4rxSqUSExHUInStlaevS3wIgmAngP8awH+4/P8A\nwCcAfOPyLV8F8C+u5xkppZTSh0vXixT+HwD/OwCGEI4AmA/DkHjlHIAdV2uk0WjgzJkzqFarBmN9\nifnl5WUTB8j9FhcXYxxazYlJfvRJpiz1kAS6qISnK59FUaHdbht3YkZemlOV1H6/UcTg71eTnefa\nc3NzEQUZ0OX23uRFzjQ3N4dXXnkFACKcmjCZcBboKej4/KSirEkKVW8G0/RtSYpGUqPRiBVjpcig\nvvsKg7nuCqvZX6IfLQDjU8W9++67ZjIkHL///vtx2223RcbCuZ2ZmYkl49H7PLXbbUMl6o3oiyQD\n8RiNpD3BsdRqNVOaEj0sLS1FYi58mx+aR2MQBI8CuBSG4fPv8/dfDoLgWBAEx65V5kkppZQ+OLre\nUvS/HATBZwGU0NUp/AGAoSAIcpfRwk4A55N+HIbhVwB8BQDy+Xx47tw55PN5O0HJvYkUZmZmEh1c\nktCAl3GJMPSkVo5KeZpoYHV11eRvckmexEEQJHJOj0D0lPf3JMmR/m/t4+rqqnEdIpczZ84Yl6e5\ndGBgwByUKKOzj4uLi9YeveKKxaLpIIgUND9CUim3pBgFP07lyrxP59tnzVZZ20exKlJQ7sf14Nou\nLS2Z/oJzwP8DPQSieQ+41x555BEAwL333mt9v+WWWwD0ys5rn5T82HV+krJ5k8trUtz1ImbVO1fr\ncnC/cnyzs7OGGmgu1Whd71l5NXrfSCEMw/8jDMOdYRjuBfAbAJ4Iw/ALAP4RwK9evu2LAP72/T4j\npZRS+vDpg3Be+j0AXw+C4N8AeBHAH1/tBzRdLS0tmZzuU3Jr3gM9eb3GVtOr+WjJJJNks9k0zqWc\nn6iBJy9dgwuFgpV+p3ZbazwkufMmuVbzPnKMlZUVQwO+YtDCwoI9i23UajXjfspxfQ1Ob7LVZ4dh\naHK1piajPO/n9moReP4+jWnQ2pA+HkLlb3J+dV7ynFTrI2phVd8/jm1ubs4QJ3UG+/bti6HAgYEB\na4NJXzmPS0tLsf10JcpkMvb8pPqVqn9ZT/cUBEEkwhfookaiDX6eOHHCTLM7duyIfG7dujWWcu9q\n9HM5FMIw/CcA/3T573cAfPQaf4/V1VW0Wi0z1XBzcOFUiabky5ip/3+S1yIpKeZAA2N8cRn2a9eu\nXVY2jJC72Wza5mEb6qWnCiGgqzTlImqwFsfHA4mQemRkJPZicCMDvXRfx48ft8PDm/RWVlZMpOCz\nt23bZqY6jiWfz8eCklQpxoNIiS++3/D6Qmswltch6YHFNtiHSqUSG4syCD3kfWVp/b+vFH7vvffi\nhRdeAAA75EulknmpkhmQNKz/Sn4B+rJz3ZOUfkl6tCQzNeeDv6vX63Zocx1fe+01a4/rSJHywIED\nJgptlFKPxpRSSilCmyL2odPpoFarod1uG8zzSVfDMIxE6wHJqbiAeISl3u8Vk7lcLlYerdVqxUxB\n9BCsVqsxJejJkyeNi1DJVa/XI5xN+5vJZOyZNMVVKhW7n/786pxFrsDParVq8JQI5NKlS8adSFrx\nidyEfcxms7HIP3UC8hmNwzA0LkXurdxP095x7nzildXV1VjJNxYXBpJTqPnIxWq1iv379wPoha9r\nGXnOo3rHcgzcG4cPHzbxhWs7OTlp8+Xv1+jRJBO3F1m1H5z3QqFgyJDikpqbSbpfvYdno9GwPUCE\nWK/XbT9xzThXZ8+etTnaKKVIIaWUUorQpkAKKtf74qaU5YGevK51Br2+QJNKJNWBZLtaaPZKMe48\n5Smjzc/Px/QNr7/+eiy6r1gsGufkM7UwqNYjBLqnviZPBXp6g7W1NesbOc3a2pq1z3FOTExEzFRA\nNBcCuTxpenraOAvb3bp1q6WbY/p3LfqradhI3sU2Ke0cf5fkjLS6uhrT/xCNNRoN+5vjHRsbM0Ua\nHbFuuOEGy4/AfaLKObbL/AgjIyO2LlyDsbGxiLs80FuzJAWy7tukHBJ+TwC9PcP9xFqR2kZS0hqu\n8draWkRPxL5xjjximZmZicWwXI02xaHAF1lfaB+/cPToUYODzDW4uLiYaOPlYlBpRJqZmYnB5Uaj\nkZjkQj3q2C7/74O0ms1mrM5CtVqNBXUlFbHh7/r6+gzm8dnUsDcaDduk3PjLy8u2+an41EKqJPZR\nc/upEpeiB2lycjKmBKP2WjX8GvTEsWjxFY4tSYTzyW/6+vpiMJkvqmZ61qxcnFsGeW3bts0YCOfj\n8ccft354saBSqcQOoE6nYy8QD5tTp04B6CpIKdZdiTj22dlZiz9Rr1uOmcrkpIA8JR/roodOkocp\n9zxFi2azmZir8kqUig8ppZRShDYFUtBQWP0O6CmL3n77bYuLYC2Dl19+2eCvigzeJEXSCECSQnOF\nez7ph3o9ktvwBG6323ad3EQ5tkZf8v8eblYqFVM60suQ3IrmWgCR2g1EEnv27AEAPPXUUyY2eNEp\nn8/b/WxLvRw5f2tra8bxyY3J1SqVSqQEGn/nlYlaft4nulHFWlKJPT5LRTrPSWdmZixSlfPxiU98\nAp/97GcjbRAp5PN546qMiDx+/Lj9lnOm9nyNNQC6Ho70VbmSZ6MmpKEimnshm83aPqHJs9Vqxcro\nJeXwVD8YL2ZoQpeJiYnIWJrNpomGG6UUKaSUUkoR2hRIgaS+3iRymrNnzxoH07h6L7M2m007acnt\n1avOm7wKhUIsfVer1TJux+8od2az2Rjnmp2djcnTSSf6lXItlEol3HvvvQCA55/vxpg999xzALqe\neewPHYneeecdS7lGObhWqxl397qCRqNhXIpKOqBn4lQORy5J7sP/r62txcxyGgmZFDPB+zUlmee0\n9Xo9VieUa6d9I126dMn0EkRtS0tLhiqpoP30pz8NoOvcw/WhnN/f32/zwfWfnJw0hMV113wUScl4\n/Hxw7GfOnLG5VdTDfeTNskDcHKtxJbw/aQ/l83lDmR6JnDp16pp1CpvmUPCKRq9FbbVaZl9X8uGs\nmUzGNpjCNiBasFPLh/kU5kHQq9CseSCB7mZVOzzQ3fDMeET7ORWOSnpI+HGura3Fsjwx0Ue1WrWD\nTUOhqXTUg0izLPt54YtERW2r1UpUYHk7P+FnkgZe4S9JlZGcU7XYeO26Zq7yCt5cLmebmp+lUsmu\nqwWBByEh9C//8i8D6PoyUOzRA8tn5tIybVxj9ST03rNA3KNRFbucR342Go2YtUdFZx/Ip34KSYcq\nGcDo6Kj9zf3KQ+Hll1/ekFu2Uio+pJRSShHaFEghDMOYCdIrHjOZTIz7qflRPSAJKclVNSmLNzFq\nwRKe3lo2jqRc0qOIXC4XM6mtra3Z85NMTr4fMzMz5rdODk1F1ZNPPmlcUpN++FyU6r2oQThAl6to\n8BDHkZRKzfuMaCyJz0ytacc8zFfupv4Sfm41RoL98D4pQG89y+WyoQENk+bz6OlJKD06OmrmXlXA\ncj40k7Um0wGioud6qdd07FRanj592trVYjbcM8ePHwcAHDx4MDanGsRGsYBIQMU1ts8+A71itlrL\nwiuHr0YpUkgppZQitCmQAtArmKoKQCBasuxK6c1UR+CdXXhtdnY25tyjzksk1T3wxKXyqt1uG9dT\njzxyLCoJt23bZok+NcEmycuihULBUAC93chVarWa/ZZj27dvX6y2Qy6Xsz555KVZfZVrJ6VQUzMp\n0JPRm81mJAMz0OVS7CefpXI+ZWjl+Fr+jX31jkR0RJqfn4/VOajX69Yun7Vz505TjJLY1j333INj\nx45FrmmBXnWY8mnhSOpJqHoYVdACMFPpa6+9ZsiT99x6662mB2LfCoWCrQfbVWRLFJCU1JU0OTlp\nkZ5EGaxbcuTIEfvtj370o9hvkyhFCimllFKENg1SYEoub5pSP3af3kq/U221z23ANpNkV5WJNUaB\np7fWDgC6HIQyuSZPIVJ49tlnAXQ5KE9v+rczwYeiFU23Re5EBxs6oORyObMYfOYznwHQde/9zne+\nA6AXQ5/NZmO6BI5DHabU5OWtGn19fWa+82gJ6FkANO8Fuba39qilQZOFeGenTCZj19kuHbIuXbpk\nXJhrsLKyYn/z/omJiYhJWe8/ePBgrKpYvV6POJ8BXfMm4z6eeuqpyLW1tbVEvdd6LuFnz541rs2x\n3XnnnYbuNKqRa0TLC3VJ6nSkady4Z7i/V1ZWzL2Z88H/F4vFa07xvikOBSoMBwYGbHPSvJQUkpqk\nJNQXjZPFTy5+uVyOZV5S6JqUhdj7PGSzWVsgKjQHBgZsYXk4qPKHh4H237+gpVLJYLqHgtls1l5U\nFmqZn583s9PRo0cBdF9oHhDsR1IhWI63WCzaRuTYx8bGDNpyzjmP7XbbTK086F555RUTX7gGScFP\nquDj/HGt1XznbfV6uGrBH86VwvCknIi85v1ZdG44H9u3b8eNN94IAHjrrbciY2k2m7bGuifZBsen\nAXc+3H11dRX33HMPAJg4o/uEz+QLPTAwEAsMVF8ezuPQ0JAFevn4lqmpqYjPx0YoFR9SSimlCG0K\npAB0T8C5uTk7/XzSDd4DRM1h6tUFdDmHogAgCsOTEoHwlFeuQ6RCaMz71QyqDjy+lNjy8rIplah8\n5LVdu3YZB2Ccw/j4uEX8/exnPwMQdboihKd4Mjs7a1znk5/8JIAuOvCl5UnZbDaW72/Hjh1mviM3\nDoLAsj0TbRDODg4OGpeimUsTxngUpgV9k/I8qujnxUX2f2ZmxuaeKGj79u12Hzn7wMBAYkwC0F13\nr9zs7++PeSNWq1XbH0QxmijHc1xV7HK/aFUqH35/4sQJ3H///QB6Idwvv/yyPZO/pcepJvQhUlQl\nuDqGUfTk+Lgme/bssTXeKKVIIaWUUorQpkEKQPdE1cSn+pnkYx8EgZ2WWsiUqIGfbLPRaCRmWNb6\nCvwkUqHMrWY8mifJfer1ukXQaSZmnu50KKGZaHV11fpNp5qDBw+afMlIS8rqOmYil8nJSZNB2e+f\n/exnxsn5LCq2dP449pWVFau7yPk7ffq0pSfj/OlcMfZCk6JQz8C5ZUZu1QMp8vPu3IuLi/Ysck2a\nBi9evBhLIFupVIwLU7cxODgYU6ip0xD1P5owRhWofDavU4bXjNM+liaTyRh6YUIajVXgvuP8TU1N\n2dz8wi/8AoAuOtD1AHroUZMQ0xS8srISq7dZqVRMv0RdCz+3bdv2n2eSFSCagCKJ1KauykUfzFSr\n1WKBLnwB1ceApM9URZJP2Z6ksGO7xWIxZn+uVCr294MPPggA+NjHPgagexA8/fTTAHrQHOhBYb7Q\n9913H4CuzZvJPvjy5nI584rjoTM/P2+wlKQHi2ab4v3cpDoWn36eIeuVSsUOJYpkmUzGNiB/R+tJ\nGIaxEG5dM1Xs+uQ6hLzVatXEGH7XbDbtQNGiN158USUu9wfb0nB3PrNSqdh6+DDwhYWFiCWH7fOg\noNjGMQ0NDZmIpVmzeHiwkO1DDz2El19+GUDvJacY0el0bJx6IPHA4iE5ODhoTElFYLZB5fRGKRUf\nUkoppQhtCqTARBrqqehRQ1LRDI0w0/oQvvio2pK9wktLlimKWC/sWUvXU7nT19dnHFHjJ3iS8/nk\n2sPDw1aOjOLD7OwsfvrTnwLoihIA8KlPfQpAFwUx5JecTMfEOejr6zMu481n6pNAKpVKJl4kxZx4\nEUrT21GxVigUYopdcqu5ublYfQZFa2o29QWF6dV5/vz5WJRmsVg0PwbfVx0LP+v1eiyqslgsxmI8\ndM8RlVBUvHTpUsREDETzRxJ1EL0lFTpeXl62VHH6O6IvL0LdfffdJkZRTOl0OvjoRz8a6UehUEgs\nOMR59Bm+r0YpUkgppZQidF1IIQiCIQD/AcBRACGA3wXwBoC/ALAXwLsAfj0Mw6vWraLOQBWLQHLC\nSnUs4snI07DRaJhyiCe/+paTAyhX4GlMWVjzDHjEoA4r5KArKyuGCtju8vKycSBWIqLs+KlPfcoU\nZOS+jz32mPmmq46AfaCZkP3SLL2KAHwBU9WJ+IQdQNxJTMu68Rrnb/v27bYubGPnzp3GxTjvdKaq\n1+vWH+pcVldXY0V+Oa9Aj+Nybnfs2GFjP3PmDICujoNl3Shfa8IdH3WYVFuhXq/HolhVH0VZnorY\ntbW1SA4EoLsGXFMqB6lcXltbi+VraLVakYQ1nAPP+TXexudwKJVKtoc5Jq1JwU8ilm9/+9vXnI7t\nesWHPwDw92EY/moQBAUAZQD/CsDjYRj+2yAIfh/A76NbX/KqlJR8RD0VkzI3+/tbrZZtUmp/NSOu\nz4eXzWbtvqRiH0lp4nUxgO4Lws2kQVjcnNzMfPYNN9xgm46HQqvVsheB2n9u2sOHD+PRRx8F0Msc\ndOHCBTvEqIRMKkWm4hLbo7VgbW0tlrhGoaYPKFtcXLQNRhFtcHDQXlCKR9z4ah+nYrKvry9WHVqz\nQvFw9VYInccbbrjBnrkebNaxDw4O2pi1gIovOFQul2PiDungwYPWXyoLT5w4YQpGvuRUfM7NzcXW\nQ/1faE2Yn583xTIPPw2r9/ksNbRd96vf1+zXmTNnPrwkK0EQDAJ4AJcLyIZh2AjDcB7A5wB89fJt\nXwXwL97vM1JKKaUPn64HKewDMAXgPwZBcBuA5wH8SwBjYRhevHzPJICxdX5vxLRTmqTDK4FU0UjS\n73hC5/P5GOwlZ8rn8zFTo2ZKTkqa4e/XDMUacutL1VUqFYOS3tf/hRdesFRrRArnzp3DrbfeavPB\ndoGuF+MTTzwRuabZhTne06dPJxYS4Tg4lxoARA6XlHKNHFS/Yzg4C9e88cYblvaMyIVcMJfLmf2c\notChQ4dsPVQ8Yn+9GU9Loj388MMAgM997nPG+ZMKz/i4Fc1hSJFramoqEjQERGNYiGw0OInXtCiw\nL0Oopes4b/xOCw8RKc7Pz1suTs7tvn37AEQV39xfmvtTyaeR06zeSSjqSnQ9isYcgDsA/FEYhrcD\nWEFXVDAKu71P9D0NguDLQRAcC4Lg2HruqSmllNKHT9eDFM4BOBeG4TOX//8NdA+F94IgmAjD8GIQ\nBBMALiX9OAzDrwD4CgBkMpmQ8tN6Mp2igiQOrSe192jUfPqes+RyuVjZOD1ZfTiwKjfJ+TVCj9cu\nXLhgpzY5EfszPT0dS6W1b9++WOk0/n90dBQ/+MEPAPQUWfpbjctI4pyetPScNydqUlQ/HwMDA+Z0\nQ24/OTlpKIYIgf0eGBgwjktOvWPHDpPNea1YLMYczvjMSqViHP2RRx4B0FXAeUcs3S9ez5TJZGIl\n/IrFYswZ6fXXX7e/1dzMfjEpr0bycsz8TkOzfX8ymYwpRjXexifUpV5FU/pplGdSEV4+l3E21L+U\ny+V1Y0LWo/eNFMIwnARwNgiCGy5/9UkArwH4FoAvXv7uiwD+9v0+I6WUUvrw6XqtD/8TgK9dtjy8\nA+C/Q/eg+csgCL4E4DSAX99oY0logKSWBp7wW7ZsMV0CT3aNZPMpvmq1mnEn/i5J1i4UCsY5k1LO\n+8KkxWIxUpodiCYS1YSjfCY5LuXkpaUlfP/73wcQTxKye/du45Ls4xNPPGHmME0E46tRqb7Ej6XR\naETiQtg3b/nh2Obn523+qMcYGhoyByhaLqjrKJfLNvfKrfhMWie2b99u7fp8DYcOHbKoQWrpwzCM\nITg1Z/ukJfV63SxS3B9aCYvRqfv27TP0Rd0GOfr58+cN4XAdL168aM5NvJ9cfnh42EzLpCAIYolh\nl5eXY/UhaH06cOBARJfAsfs11hT5dFsnFYvFD9ckGYbhSwDuSrj0yWtsJ7HQhn5qliVCwSAIbIOp\nb4L/rW5y73moihhuupGREdtE3tut1WrFQoWTfBey2WykjoT2UT34CCf7+vrwi7/4iwB6m0J95n12\nqHw+b/Ogtm99hrbBPvE+3pNU1o1/+0rGrVbL4DJpZWUlcggAPS/ARqMREYHYJvvN391xxx3mwcgD\ngHEiBw8ejNRZ4HzrCw90DxquqQavAd39wn5oDAuVoFoejwcVD3K+7P39/RZezoC1ixcvWl5IFadI\nvhZDNpu1+BbNPE2vVvaHofMHDhyweeMhvLKyYnPF7/r7+81Pggcz56xer0fK4W2EUo/GlFJKKUKb\nInhEiYcAACAASURBVPYhyQkpSennvxsaGrIIPi1Pz9Oe3IEndaFQMPjG7zSrr3r38ZTXNFhAFBVo\nYhXl6kCXW5GLUVQhB6vVaoYGNMkKOSchNPvzjW98w7wilfuQ49KEtbKyYuNiDIQqJn0ouVY9UrGN\nXMyHCtdqNTNFckzz8/OGKLyJr91u2/MJdYeGhuy3Wt+Aa0anJM7n9PR0LFR4dXU1kvyEY+NzvVJx\neHg4VlyXcwD0vC3PnTtnfSPqUYUw26Coeu+995o5kQiA95PbKw0ODprZmWOpVqvmfEb0w3V/7rnn\nLJEOEcibb75pc8Po1AsXLpjplOuuIf+aRXwjlCKFlFJKKUKbAikAXRnI+6cDiPl+Az1ufOHCBePy\n5DB9fX2mbPGKSaBnGvMRgNpuX19frAAoOUixWDQ5UxVa5IiKIshteLJrlmTKhfzkqc9n6P2ZTMb6\nzcQqKysrZiLjp/ri++Qza2trMQWVpkHTJKp+zjWCkco23t9qtcy9mTEPHPf09LShH47z4sWLMSeg\nxcVFW0fqLMiNS6WS9YNzMDAwYGOgfL9lyxabNx8vUygUYunKBgcHY6h0amoqVriWbdZqNdN3MD/B\njTfeaOiBHJr5LGZmZgy9cj63bt0a0YfxGtf+lVdeiVw7ffq05dSgviuTycTc9zXylJ9EUpqIaKO0\naQ6F9ezraoXwSTSWl5dts1GZMjAwECvQoUU6vWJN04trMBM3BcUHUrPZNAjPzT8zM5MYfpvk+cj/\nc2G5EXbs2GFig+YMBLqJOD7xiU8A6BX0OH36tNmi//Ef/9Ha9XBd+5W0YUh8ZrlcjnmCchxTU1Ox\n9OI6Hxrey/9TtOHLUK/XrX1C6JMnT9pvaMkg5D106JCJfFoBnAE/fCknJiZiORE1II4vO9d6YmLC\nRBbC9cnJSROZeBBqHkRfqOa9994z5fBjjz0WaeuBBx4w5sEDaWhoyMbMucpkMrjlllsA9DxC2YeB\ngYHIgQJ0D0YeppyDubk5E0P5LE3dr4xvI5SKDymllFKENgVSYLIUxkAoqZefz6armW01954vHqJJ\nSHiCakEXciANOyYc5PNpf9acelr8k/2gIk5NgUmFRsl1eLKvrKzEalyw/3v27LE2yBmXl5fxta99\nDUAvJFvz93mkUC6XDW5ynPl8PiZiac5Kn9uv0+nEkoosLCzYnJL7abwAPewUfVD0oOnyvffeMyUl\n55nP1ngEtjU8PGyKPLaxf//+RN8WoAuzOW+ayITPIkefnJw00UAjJ/k7tk8U+eqrr1rxGMZ/ELUN\nDw/HfGhOnjwZEeeALurhd1wX9baluMZ5LxaL1jcqKBXhaJwF0EUuSdGzV6IUKaSUUkoR2hRIgYlS\n1XffJ1vJZDJ2ynvnFCBa3stHrPHkXV1djcQJAF30wBOd362srEQ8zoBo3D6vUX5XRSbl31arZc/3\nip5ms2mci/cnxRyQY1+4cMGcUijPXrx40Tgy+6byo5erBwcHTfbXezQvAhAtH085ltyq2WxGFIyc\nDz6D7XKO1cNS5XG2y2dp2jYfrTk9PR3Jss359lGBaorWpClAFxX4WgmdTsf6wb7Nz88bAuHc0ySo\nplquWbFYtNwKv/mbvxnp43vvvWf6BdLZs2fNy5EK2Hw+b2iEiIJr3Gw2TWfGZ5bLZUNHP/nJTwB0\n97mPyOSYdK42SilSSCmllCK0KZBCUuJWL1+vrq7G0mxpMlI1z/jaDqqF9im7Go2GoQGe0Jrth8Tv\narVaxOUU6HIfTQdH4nM1RgLockFaS9RFeL0U5cVi0VAJC58eP348VgVK08iRC6ruhKZLLZbLPvFa\nq9Wy8RFZqO+8T6WmtTR8Lcck/cT+/fttzNS/aJp4TT8PIOJWTZQyMzNjY6DGfmBgALfffnukDc5Z\nqVSK5cUYGhqy9phy7cKFC4ZG+Vs6Jx0+fDhWB2Pv3r2xPUZk8dxzz8UiOHW/cv01wataOthvogda\nF7S4rurH1BrE+/jMa0UKm+JQSBIfkiY0KWTUixmaesqb1Mrlsr0EnGw9FLjA4+Pj9ixuSm6qnTt3\nRsyZQHfDcxNxUbTUm8+UrF6RhHl9fX2xiti8f3R01K7RDLW6umpwmRssCAI7DNg+U3ydO3fODizC\nz8HBQVOycRMtLi7GXmTO6YEDB0zsYX9OnDhh8+cVqu122zYz12x5edkOSR6k27dvj9Rj4LoA3YPa\n57+8dOlSLFfk1q1bbQw+JqRer8e8+rZu3RopJwh0q0LTx4ViAed7YGDAkqBoYRvN1wj0Dte7777b\nDj9vatS5LRQKkT0D9JSt4+Pjdk0LFnHM9OY9fvy4zVdSaoAPLXQ6pZRS+i+TNgVSAHowJ6lkOICI\nuVKj9vz9WinIVyIqFouxCEe9Xx0/eJ+HxidPnjSoxtoD9XrdUIEqJMkZyEmpLFJIR86upk5VZJEo\n2nz84x8H0EUH5CzkkAMDA+Z1R2iuEXg0XaqnIvtGuN5sNo3DsW9qQmQ/OC9btmyxdr0Y1mq1YlF7\nzWbT+qaiH+eUcQh8Zl9fX6wocLFYNJOxohjCas4HuatW4SIn7e/vt/5SmXfXXXfZb3xx3RdffNGU\nkJzTQqFgClruBXL5kZERC49/5pluHiJVlmuKQI8GNBrY16uo1+s2R5p20EcZa/KcVNGYUkopXRdt\nKqSgsfxef6CKSEUF/I6nbLFYjKRJ8/eTi/AE1rTvPMWLxWIkpbb2J5vNxsxhquBTs6JHG+r0RA7E\nBK6nTp0y//Y77rgDQM+vv9lsmhyu3FBrHgBdLsI4BCoHNTmqT2Wv8STqDs2xk5NS7zA3N2eckS7Z\ne/fuNX0KOS4RwMjIiLWlhXE5L+Sqly5dsrEQFXCdzp49G8s9keSMND09be7Cap7m/727ehiG9iz2\n/7XXXrNxsV0ihqefftrmjxz68OHDNlZGvVJhWi6XY3uz3W7H9EVAbx14TR2WfELgbDZrlcR8dS8l\nnyToWmjTHAp8wb04oAeBV6IkiRv5fN5gr89oo0lZeHBoMInayrkYfBlVYZaUj88rRrXKs9qMSdx0\nVEJNT0/by8dDQRPH8FlM9HH+/Hn7La+trq7aBueLx2drAhaSasPVE46ect7er/3lS1ur1ew7vvh6\n4PosVaurq/ZS8dr8/Lw9i3NLBa8eZlT01Wq1mMepZhzii0mL0TvvvBML/Go0GrHq4Ry3jo/zqUlt\nVLPPw5cHoq4dFZmqaPbMQ2NvvK9GuVy2Z6pHKMVcTTfgc22q2HGtAVGp+JBSSilFaNMgBZ526/mv\nJ5Wp1xOSn2qCIYfRkFqeuISf7XbbTlJyDiDqSQlE7f08jdmWnsS8v1QqxcxEGiVJDq5+6fyOZjAi\nnOXlZeNO5EjZbDbmvZjNZg2BkEuql6GGAfN3ScltFBno3GazWfubEZqLi4s2fppIOVczMzMmEpHj\nlUolE2kUnSTlziRp6jySL/k2MzNjCsA777wzMqbV1VVDFlxjzZTMuIu5ubmINybQW+NOp2PrR9Ls\n4ERL7MOWLVsMEalI7LNWq++MF50vXbpkZkeNBfFidNK7oehEEepGKEUKKaWUUoQ2BVKgLqHdbscc\ng5JOVEUHXsmiFaJ8JlxVTGpUIJ9JDtZut43D8YQmNykUCon5BngfubEmbiXp/eR+5OSlUsm4KTk1\nZfROp2PX6Ix0/PjxWHxDs9mMoQH2a2Vlxf5WzuKRlqaWS1L6+ijMubm5GFJQLz8+k/M5Pj5u88x6\nh1p3k/ezDxoTQvSzvLwcqd8AdLmqptgDEMktQZ2QriN/y3XRalRMr8aoyZmZmUguBu2j9vvYsWP2\nf59FWYvgJiUT9m2dO3fOEtdw/bds2WL95bzU6/WIYxwQzUOihXw3QpviUNDQaf1OP7PZbCwzk6b6\nJq2srJidn4vHSV5YWEgMqvKeh0EQGBykmKGuz3zhNCCJLwsXL5vN2ub0tu92ux1LdNJqtcz+zA3J\nbFLFYtEOCK0izQNF502zGwPRF9vDSD0kkw5OblbNDJwkbhBy+8O4WCzGXJ9LpRJuu+02AD1firW1\ntUgSFu23rjnXIggCm0vC6/Pnz0eeAUSrjfsQeyUeBO+8845ZH+66q5uknKHRdHcGkvN7cj4oPszP\nz0cyZ/n7NUOXKmaB3n65dOmSHWbcfzMzMzGxUZ/v18zP4UYoFR9SSimlCG0KpAD06jp4RaP6GPCa\nnnz+hGw2m2ayIayliVJNQprTziMKTRnmTXYDAwN2amsYtleeaYISVYIC0cQaqrxSuzrQ87CcnJy0\nkFsmMmm322bnJ0fS2hhJ6dg8KedST0wtIccxA9HCJQp//Rzxd7qeah5mX8jlJycn7bdJcQtEd0Qi\nBw4ciCWA6e/vt3gFojyKLPl83sZH236tVrN2uQajo6MRHwQA+OIXv2hzwCQvVIpu2bIlFpCnQVtE\nfiqOaQwDye95IoHJyUnzY6GnqhYR5vo3m83Yel9rYhWlFCmklFJKEboupBAEwf8K4L9Ht7L0K+iW\njZsA8HUAI+iWp/+tMAyvahOhedHHLSSZJLXYq79P5WuazcglxsbGLMOucn6fjm1xcdHa5W95eutp\nT+6X5FFWLpcNqWhtAn5qeyT2iU405KQTExMmW5JbraysxOpD1Ov1mCOMmki94ksdrDQa1XMujndo\naCjRi9LrL0jbt2+P1ZqYmZkxfYCmH/Nh40QbiiyoxNuzZ48pNYnQBgcHDRkQ5XnECPQ46JkzZ8xj\nlOv/sY99zOaS833w4EEAwO/8zu/YWL7zne9Y34gMrlSkWBPKajUv3q+KYu1jLpezOeK+PX/+fCR+\nA+giT49sqX9ZXV2Nhdhfjd43UgiCYAeA/xnAXWEYHgWQBfAbAP4dgH8fhuFBAHMAvvR+n5FSSil9\n+HS9OoUcgL4gCJoAygAuAvgEgP/28vWvAvg/AfzR1RrSKlFAsmklqXqUz6OQy+WMK/GaFi+l8wq1\nxGEYmpzO32k9BHJ0lY09ilhbW1s34az2l31MQj1qkSBX4LW77roLv/IrvxL57k//9E8tziEp6arn\nWIpctJis19Q3m80IEtM2tm3bFtPGLywsxO7j78vlcoQjAl0URm6mEZHsGzk1uX65XLa5JOc/d+6c\n6YG0niKtGh5FAD29CE2SCwsL5krNuIVarWbrzZTt1C3ceuutls6druZTU1Mx6xf7k8lkYvU86/V6\nJLIR6HJ2cnWPKPv6+gw9aikDIgRfzQro7UmOV83UG6X3fSiEYXg+CIL/C8AZAKsAvo+uuDAfhiG1\nHOcA7Nhge4l23CspGjUbMWGWFhP1pb8mJydNSUjl4+zsrG0eFVV8nke21d/fHwt1BXoLSq/EUqkU\ny6iTpOxj+wqTubBULk5MTNhhxgMuk8lcUXwh6TiuVEyWY9c55Txr9Wl6BvJ3ak70npv3339/zCR5\n7Ngx67eaB70tXTc154gvklZ75qFTq9XMh8MXhdm2bZvdx+dUq1Uz+bIkH8cG9A5mKnH7+/tNnPul\nX/olAMD3vvc9Yy7cYxre7ZPm5HK5yLiA7rxzvnwgF8cF9HxWarWaHeS8Tw8PzXvJNn1ho6vR9YgP\nWwB8DsA+ANsBVAB85hp+/+UgCI4FQXDsWjPDpJRSSh8cXY/48DCAU2EYTgFAEAR/A+DjAIaCIMhd\nRgs7AZxP+nEYhl8B8BUAyGQy4eXPGFJQ8sqoTqcTU6wtLy/Hir3yJJ6amoqUbmMbPGU15No7gfBa\nf3+/cSzlLElcgc/3Sj8NieU97XY7ZpIkN3n11Vdt7IykVKcXn6Fa29DvklCYF4WUY/vIO42I1LoI\nTMrik6w89NBDJppplmOuwa/92q8B6BbQpSMT2+X96hlIuH/+/HlTEpIzvv322zYWcmOKBYcOHYpF\naw4ODhrCobPYsWPHYqnzXn31VQBdTn3TTTfZ8/kd946Pqs1kMpGQaRL7yDnS2AQfz9HpdBIjIr3I\nNzg4aMhAPYCBrjijKQo3QtdjkjwD4J4gCMpBt8efBPAagH8E8KuX7/kigL+9jmeklFJKHzJdj07h\nmSAIvgHgBQAtAC+iy/m/A+DrQRD8m8vf/fEG24skofAKHDWVaSorHyvRaDRiCSa0bh+VOjTntVqt\nmCyvCjvvnLK8vGzP58mufVMlp08HRiVXtVo13QM5jaaA84k8T506ZSYyytKKWDROxMcreHSlpEV9\ndc58chpem5+fN0UZ0cFDDz1kUYl///d/H5mrdrttsvBrr71mc8Y1YJLT0dFRG593tioWizE33ZMn\nT1pMAJWLFy5cMMUr55Tcc2ZmxhSTRBH5fN76xn6ovsPX3picnDSuzd8tLS3ZWOlcRJqZmYkhPx0H\n+7i0tBRLQkvSRMZUKi4tLUUS9QLdvcx+sI+q87nWRCvXZX0Iw/BfA/jX7ut3AHz0WtvSkGT3DABR\npZgGgnhFlsZIeD8BDbXWCsYk2s/ZHyCaCZrEF0MVd17J1mw2Y4cY75+dnY1VpK7X6zGFpCr6uMG0\niKv36VCric+srIE37IfmAFQlKPvLl1dzWPLg4kt24MCBxJgKoOs9yHnWkG72kbkLP/OZz1iswfe+\n9z0APfGhUqkkhtP7wi+7du0ypR8TrrCvmnyGysLFxUUTWfSA5vz6PJnFYjFWko0ZyAEkepd62N5u\nt2MFilQB7APthoaGjMlQMd5sNu1Q0mrgXuxRkZXruFFKPRpTSimlCG2K2Aet+5Bk7vOk2Zl5Cuop\nqwVE2D4Qjeijt6OWJOcpPjMzY1COp7GaDn2J+aRw6nw+b/exfU3Y4hVU/f39sVoQavP2nnCKCjxi\n0L99ARPtRxAE1m+9j+0dOnQIQK9Yyl//9V/H0pSdPXvW5uruu+8G0IOw9Xo9Vq5t27ZtZufXFGx8\nBuH7008/DaDLIYmqyMXvuusu6y9Nh8PDw+b34EvPqSmVZt6xsTFDAeS8pVLJ9hPnSPch14VtBUEQ\nCZUHembWvr6+iDjKe3wZvVarZWtAMZN7aHR01Nqj6MkUAwAi4iZFICILimPtdjvN5pxSSildH20K\npKAJRL08rbKRJmwFuieq99bauXNnTMFIIiIBeicqlTZA1MuMnJwKST5HUYTKgkleY756lZoafa4F\nLU+flG2Z3Iyk86El0XwZs6SUXeTa/f39sajR4eFh49Y+gWur1Yp42wFdEym5POsc0Gxar9cj3oJ8\njs/OvLi4aGP/zGc+ExsTdQT79u0DANx8882GHshdT548aUlbOFdahs1nph4eHjalLZ2XNNO0j2bM\n5/Mx/VWxWIx4ggLRpLV+vlW5qePzilTuvampqViUaRiGtu5Ubo6OjkYK0CoFQRDbO1ejTXEoAL2w\nZg0aAqLuwklp39WeDXQht4buKumLpIoeKnHUN4HteV8HbVcVo1Q06XdcKO82rAllVNHnU6uTNEkI\nN19SOTAtR+f7CiAGdQcHB01hqHkb2QY301tvvQWgO38qBrA/HBeLpRDyDg0NxSpj12o18w/gtVar\nZenZCYN50LRaLXvZafHI5XL2W/oMvP3222Z94J6hUrHdbts1zu2FCxdM9GBbWh5Ak9lwnJrBmvPD\nOeU6MjR7eXk5ZgULw9Da0PqiFBO91WlsbMwOMd4ThqH1kSJcvV6Phf/7A+xaKBUfUkoppQhtCqRA\nhY2aJX1iEi3vpiIFr1MRc/HixSsmmCDc00IuyimALtf3lYt9YA/QO40LhYJxAHIiNQORsxCuZjIZ\nUwSRrtZnj3A6nU4sx3+SPZrXqtWqcU715yea4TxqwRdvUh0ZGbHf8lk33XSTcWGWjyMX1DoTnI/F\nxUUTGzinu3fvNqTF+VXuSQ5Krjk2NhaLNVhYWLD18+bboaGhmM/FysqKZc3WgCTNaan3nzp1KqYI\nPnfunCmRqeTk/lJzucaV8H7GXWzZssUSqRCVcp+vrq4mpqfz2conJiZiCWN0vxCZbZRSpJBSSilF\naFMgBaDLIdT840tpZTKZmGyuxNOw1WpFCoACUe6qnl5sl9/5RBVAPNxZE2tochaNSWAb5LSUl1k9\n6PXXXzfHGa8M1TZ8v/Q7jeQkJSW+VYclr6toNBqxmI1yuRxBBjoft912Wyxqb2RkxLiqL+zabrdN\nR0Anprfeess4MxFDX18fXnnlFXs+0HNiWlhYsH5Tp3DLLbeYo5kW6PWclveoMk/RpkcgjUbDOL6P\n41hcXIx5vmodDN5H5d/i4mLMkUzb1UzTnF+fsEX3GttfWlqyNeBc3XzzzTEdFXUbHOu1UIoUUkop\npQhtCqRAk6SevN51VsvIq1uvr504NjYW00IrB/VmzSTUofUnvMt0oVCIVSzSmgo82e+66y6LCaCp\njlF23/nOd2LmR9WFqHMWEEUuWsDWmx/VqqGcH4jWNiTnmpubi0XtNZtN0ykwPwE1/Fu2bLFnUnN/\n6tQp0+dQZ8I1KZVKeOSRR+w+IFoHkmjprbfewsMPP2zj4nwAXc7qrTK5XM4coDRVOq/7cu+an4Dr\nOD8/b/cRKVSrVdNl8DdEJ7Ozs3Y/kc7S0lLiniT5vaW5IbiHdu/ebfPhHfd2794ds8a98847sXY1\nMS3HrDqFa60QtSkOBZZR0zyCPjhkaWnJJpQbeGRkxO7nhh8dHY1APiAK37xNWKtZK8zy4c6Ev51O\nx9rlZGtm4IceeggA8IUvfMGUSXxZeBAcPXrUNgIVTwsLC7FQaB/WrGNpNBqRA4J9XS8gqlAoxGzp\nYRjaS6AZjzgGwlS+BFu3bjVlIg/enTt3xmopUKx64403IpWlga4ZlJ6SfKGefPJJMzuyb+wP0HtJ\neDh97Wtfi8S/8HfeN4P90uAkrWHB+/msUqkUCyHngXf27NkYw9LgOyotr/QCaqYw7u/x8XHzw+Cn\nBlD5GBn1U1CR1sdP6P0+N+fVKBUfUkoppQhtCqRAJWM+n48pBzU8mfBQzYn0OFToRc6ssQNsM6kU\nGrmvclCa6nxNg7GxMYNo6vDDRB0f/Wg3QDSbzdoJTTjNsd12222xikK1Wi3GFZLSrakZ1HMFdc7y\nzk7Ly8sxhZOWryNHbzQaxpHZ/uc//3mbT6+MnZ2dtXmjyZPc9bvf/S6+/e1vR/p91113xcYHwMxy\nXslaKBQiZkega/pUJSXQVTRSacs9wb4uLCzE5qNUKhn6Y/7DRqMRS9qjZex8wWAtZcix+3B5bUth\nv6aHWy8JyvLyciTxD/ut5QSBqPjqi9omlVa8GqVIIaWUUorQpkAKQO8k5knN05CcrFAoxAq2anZc\nctlGo2GnK7k9T2V1plGk4GMNNBEIicoozblAzlGpVLBnzx4APd3GG2+8YbIzOQURTKvVMtPVxz/+\ncQDdeg4+rZomjrmSy7ZmcOZ3nCuN5OT4FXF5LtLpdPA3f/M3AHqJPWhSXVhYiMSKsF1GRVIx+cAD\nDwDoRiRSwch11GpQTz75pD3H1z7QqFPPSRuNhnF3TWtH7kzdjRbb1bUHutzVu2CrotbXT+jr67M+\n0aX44sWLhhp4H/uv+p0kUpMx++kRRbPZtH3NPa1ol/25dOlSzGFM98u1pmPbVIdCNpuNbX5NX86B\na6gwNzh98blBgZ4Nm3BYtdAKs709OZfL2Qvhk2IUCgVbPB5Iaqvn87PZrGnQeY1KxfHx8YiNGei+\nGD5rDjerHoaqvfaWBn3BfTBWuVxO9MPgfTzsstlsLKiGBWjK5bIFJTH1+djYmCnIKE49/vjjNhcc\nMzfr/Py8hbZrQR6+XFxPVQImHVwcixb01Zeb/fWUtO6cv7W1tXW9ItfW1uyZWjqPc8n508NYA8n4\nna+croeC91qdmpqy9jQRjL9PAwN9+51OJ7LvN0Kp+JBSSilFaNMgBSaPUD9xIGpiIWfhtdXV1Vjx\nk2KxGFMOaplzn5BEIbf6x5PbEPYqrKUYoBGUbIOmt7W1tVjMA0/7qakp8zjj/ZlMJmaK1EKvVKzR\nlNnf328ci89RpEWUdODAAXsOkYgmVuHcUOk2OjpqfxMt8f+FQiEWPbi0tGSIif25/fbb7dkMnVYz\noVegTk1N2broWDiPmlKO/SYplPZ5NXlfkoJZM1kTmq+ursYyKrM/mnyGoq2KnhwTRYtisRip7eBJ\nE6t407miDe4TisLqn8I2Tp48Geuvol81aW+EUqSQUkopRWhTIAWaBdWfP6nsvE88qkk/yGXz+bwp\nw/ip3ohJ6bB8WXMtJecdiLQOAc149Xrd5GRyjJMnT1rf1VGFz9TkqUAUDfgMy6pn4f0PP/ywyfrk\nJprjn5yFClCtE8HnbN261RAF+6il5YkA6LA0OTlp68LCq1u3bjVT4Y9//GMAvXoLn/3sZ22e6ezE\nsQI9PdDMzIwhpqRkO16G1loJqm/x9T64ZlrEVe/1CVGCIIhxVe+YpaR6A58uz5dB1N8APbTx9NNP\n2/5jLQtSEATmOapejz5xjiIttk9dzvDwsI2T63I12hSHAtBbcJ8WW2ERN3BSTkJO7PT0dCw7kN7v\nlVbZbDZRO5tU1w/obkLN6Qd0DwKvJFRPSSqBmKykUqnYC8FNp+XuvKKsVqvFvpuamoo9U60mhPya\nkp0uuyR1E6e/xMzMTKyIjSqxeI2a+127dkWCe4Ce3f8HP/gB7r//fgA9Ze+OHTvMj4AenyMjI3bw\nUEnJAz2Xy8XEhyTK5XKxoCD+Tr05tcamF6f0wNfANpJPqMLnAj3YzjWu1+uJjI37yFuCgN5hrYFd\nPiN5JpOxZ/HawMBALOUAmYeKmRulVHxIKaWUIrQpkAKhlp7oSWYUnqA8URXuqY89uZgv+qp1HxRq\neuiVz+djSh/lEmxfYwk8slEFJoltsP9Aj0upfzq5iKbn8hzppz/9qXEAooe9e/earwDHzHbPnTtn\nEJTms3q9HoPQ+XzexBzOqSIFXwZOC8R4Tv7222+b6fLee++1uSJCIFqan5+P+aCQFOYnhQCrMs/7\nGNBUynEBUR8Ar9SsVCqxuha8B+gFR2lcBufepwUcHR2NJJZhWwwl5/47depULBBK0az329CkwxMv\ntQAAIABJREFUKZougGhNK6FzvFfyl0iiFCmklFJKEboqUgiC4E8APArgUhiGRy9/NwzgLwDsBfAu\ngF8Pw3Duck3JPwDwWQA1AL8ThuELG+1MJpMxBRlPfXW4oZypTiHkqjw1NVmo1moAuhyasrZGlXkP\nwlarZdzApyvT2gC8NjQ0hGeffTbyTFVC+apU5XI5liTk4MGDdtqzvxo+7EvVlUol6zdRQalUwrFj\nxwD0lI9avJQmRt6vFYi0XJ+vNcDnLC0t2byRMz7xxBOxdHaK8n74wx8CAL70pS8B6CorqTfg2IGe\nboVrzLY6nY6tS1IRXM5HLpeLxU2wrzt27IhFx7ZaLbuf14aHhyO1OXT+dB6SnKP877Zu3Rqbv1qt\nFoupKZfLMTOimuXVFMlrfg+rU5R3XtL2NkobQQr/H+Il5n8fwONhGB4C8Pjl/wPAfwXg0OV/Xwbw\nR9fUm5RSSumfna6KFMIw/FEQBHvd158D8NDlv78K4J8A/N7l7/9T2D2mng6CYCgIgokwDC9e7TlM\nH85TjSckOVKtVou59fb19Zm8RpNXuVw2mVij5IAuFye3UTmZf6vzko+HoMY8CIJIPgfeQ427yrGe\nY+mpz35rlB9lfvabnKharVofKRtPTk7auDShKE2j5Fj83fj4uPWXMv3KyoolK2G7mhaP2nl1/vL5\nKzTNuefknU7HEBdrK9x///2GFFjm/YYbbrBakkRH7I9Gm6rTGrk1+6ZmSo2i5fxpTAL7SPSVlBjF\na+wzmYy1x/EWCoWYi7wmOfHu6opAtXYD42p8CYNSqWR6DPaxVqvFnNtGRkYMBbINziPn8Fro/Soa\nx+RFnwQwdvnvHQDOyn3nLn931UMB6G5IQjr1CyBpOC3Q3ei+CGq5XI5lq9EyZr4isXq28SUcGhqy\ngCVCNUJdrXStsI9eizwU1J7MDcngoFKpZHZk3vP2229bn9gf2q1HR0cjNmkgmqmJL82FCxesT17p\npmIPnzM0NBTzzmw2m5ENCER9RrziS2t1eNKgrRdffBEA8Mgjj+DBBx8E0DM/PvDAA3j00UcB9F5G\n2tTz+XwM+mushMJkD501OMgnmFlbW4sltWm32yZaeXOsZv7STM9aHRvoiWZJvhFAb+9qiLUXM///\n9q41Nq7jvJ7hkksuKVIkRUVvKHKsWJAF1RaCQkn7I6lb54E2RQGjiBEgbhIgKBAgaVGgiJEfQX8E\naJGibQq4aY0mDlAETtLUbQyjrRunAQoElts4rh960JKtl2WSomiJT+1yl7z9cfcMz/1mRFEPrrbt\nHEDY1d3Le+fOzJ3vm+9xPh07bs3U1W2TuxYXF4OxInRxWitu2fuQZVnmnLvhihPOuc8h32LcMLFk\nQkLC+uFmF4UJbgucc9sAsIjBBQAalrWzeSxAlmWPA3gcADo7OzMboGKjtnp6erwk52q8vLzsJbjS\nbSnnnqKnp8efzxV4cHDQu5+o3lcqlUDqaCo3jYTUQGZnZwO1PcaXSGms2gnbEWP1pdQ8f/68P5/P\nxucBViS/5k+o5gQU8znY7pGREZ/1SDqxWKCL5iFwy0I1uLOzM4gutJJPn/3FF1/Ehz70IQDAT3/6\nUwC5VvDEE0/437Xdev+Ya9JGf7JNwIqWeeXKFd9GdUly66HRnJoHodfQaFtLh6bt0GM2OjfLskCr\nunr1qu9zu0XU2h48p6enJ+AP1e2UdWvejMC9WRH9NIBHmt8fAfBDOf4pl+MwgOm12BMSEhLaB2tx\nST6J3Kg44px7C8BXAPwxgO875z4L4CyA326e/s/I3ZGnkLskP72WRpBgRVdGG8zS0dERVPlZWFgI\nYtVnZ2eDgpr8//bt2z2jMqVld3e3l0SUfmNjY/67hsDynjY+f25uzu/pVFJYbYNSXCWu1v6zgVXU\nAM6dO+e1GLZnZGTEt5HSbXh42B+j1KE2oVmmWoKd5ePZR+Pj41Fpxue0huDFxUX/XLYEfIzo46WX\nXvKawkMPPQQAeO6557yRlVqgGhqty1iDxQitYcGxiJHPsD86Ojq8NsK5pnYGa7Oo1WpRw6RF7Nmt\nK1PR3d0dtFc1BWsf0TZpXg6vEbO13Cgd21q8Dw9f46cHIudmAD5/Qy3ASmRiuVz2HULVTlNj2bmc\nwAMDA74z1HdrK/XyGmQQAlY8EnNzc/48fpJuHigm1QBFY5sOBNut0Wm2FJwt0KLHlPTDFlWp1+v+\nhePL2N/f79vLF6lWq/ln59aJE/7q1av+xeM1JicnfRITcx/m5+eD2A+2Z3p6ulCEl9e3RVgI9Z8r\ncxBjKA4ePAggL/zCLZyyJwPFRUHVZuU4BPKxswKCYzg0NBR9MWwkq7ItW89RzFgXS4hSj4NN5ALC\nF3Tjxo1+cdL4C97bPlOtVls1Qcymts/MzKTch4SEhFtD2+Q+dHV1obe3NyiTpZyNMZXectqXSiW/\n2vM3qsvHjh3z/ltletboOaCoDVgDUmdnp5dAurJbaXa9mHN7vuZlEFT31R9O7eTixYuFku98XmtY\n0jYowzCQ9xU1Jv7d4OCg1xQowbiN0efVnANqI7bI6caNG/04so+HhoaC63Z0dASl6qi5jI+P+980\nQlXjKngNakA8T2tC2HHUtHvOOSXGsWQ/y8vL0ahIq1nEIlp1TOwWsVQq+WemOztWTFafhdCtjjWu\nquZwo3EKSVNISEgooC00hVKphMHBQWzYsCHYQ3E/VK1Wg8xJ51wgFbROAAOEKK3Gxsa8dNLgG0u5\nViqVvNShpOD11QWnxC62hNtqUK6FmHvLQiMs2caJiQkvKeim7OvrCypr6XUpcflsU1NTQc0GIDTU\naQ0Oa2/o7+/3bl72s9o91M0L5BoDj/Hehw4d8nUfLFHu5cuXA9duo9HwEXxsqwbwWGN1qVTygWOa\nw2IZm9WwZ4O0hoaGogzjllmZv6lhULUJW6tDbTjWnViv1wvzlJ/WXarf7fvT29sbkAhdD0lTSEhI\nKKBtNIWNGzeis7Mz2Ldx9VcCT1rbN2zY4HPylQ/AhjlTO5iYmAgkNF2hQDxkVu8PFFdvzUyzQUMK\nG/QCxN1T9jx1afL5NKiH99RaDJb3XzURS7OmfAq0tShBKfuZ4dYDAwOB9qXaic1m7Orq8tdQWB6I\n/fv3F0qnAyvSm4xN2j/AiktZg5esC5jP/s4770TrKfIa9HhcvHgxYACjlN26dWuhtD2Q8yvQo8N5\norYwdUEDcTtTpVLxtp5Ydi/nsGagcoxjzEv8W/5fa2yuFW2xKCwtLfm0XKql7EiShqh7jwPR09NT\ncMcBK65GYEWVI/9fvV5f1RCnbkfr2lFDVYxHMhbRZlNW9fqWQTrmtlJDla1p0Gg0/CKpdQOu5RNX\nw5e6N9l/Opl5DU1H5zmcpHz2UqnkJ2cs4s8WsKVBWdu4Y8cOXxSHxjZVeW31cBUeuoDa85Swxxrb\nSqVS4NPXRZKgAJifnw+EQaVSCdzHXECnp6d9kp7OCS1Oy2vwJdd4EN6H2ySN27BG1lKp5K9h3eB6\nr7UibR8SEhIKaAtNoVKpYP/+/Thz5ow3eFGyULXr7+/3NQyOHDkCIM86pNbAFXtpacmv1jEJZqWD\nugJV0mgVIP3s6uoK4uhXC4zRv1WDk9UiVNIR2lZKAFVFY7RcloJODWvUKFTy8zxKb60dEatQpe3l\ns/FZbUCRqsu8/pYtW/z9eWznzp1eItJoynlw4MABrz1ov9sSeyr5bcFg7ZdYxJ/NRLTXZVvt9kS3\ncjzGvlpYWPBzSNO7z507V+irjRs3BoZX3ZayX5SOTzVOtp/t5HaamJqaSsFLCQkJt4a20BT6+vrw\ngQ98ANu2bfNGMxqvSBQ6OTkZlDDv6OjwGgKPlctlvx+lRNL8dBs3rtJZJbmtMqRuNmon2o7VstIs\nAcbMzMyqIbDWpsC26/lK9kLp0NPT48lgYjRy1BSU0i2mFdhMVc05sa437VNKSbart7fXS0QtU28p\n9/r7+712wUAejvvu3bs9KQxRqVT8fp3U8HNzc0HewWpGX9XWlDyF88lqFpVKpVCwlv1n645o+Dyh\ndikbeq+ZmZZst6OjI6gFooV0NW+Gc8zm6szOzq6aqxFDWywK1WoVR48exdLSkmcCstZUAJ74hCw9\nx44d851GFp+RkRE/wfjyxqIGVUWzzM2lUqnAewisvGQaB8EBVguvXldTcoEiI6/dPujAWYOZqrpU\nWbu7u33buPjdddddAZejLma6RYjdl7AJTmy3RjSqN4R9pV4h/j37lgxCpVLJW/F1UWDRE57PefDK\nK6/4Z6DnY9euXf4Z9u/f7+/JdtOwrCXreD5fbB4H4klM1t8fY2fSceHvXMx00YklJ/F83Zpp8ho/\nOYf1Phq7A+RznouCZZ0C1oejMSEh4f8R2kJTmJubw5EjRwq+XRuVVi6Xvdp0+PBhAHl8PKUHV+g9\ne/YElYr4/+7u7iCFtqenp/AdyKWOTR+mhKlWq4GLR1NzVQLwu2Vp1nh3rvZanswatNQNpdqBjbrs\n6ury2wdqDyqJLDmHfme/LywsBCzYvIa6gDVC0dbG4Of09LTX5DgWZ86c8c/FzMh6ve4NjNQy+Ezz\n8/O+PRzjEydOBHkWg4OD/l6cJ+p6ZZk7zRC1EbKaY8J+5lYh5q68cOGCv4at5FSpVIKIUJXY6ra1\nFIR6Hp9Zxz9WAs9qQmx/rVZLdR8SEhJuDW2hKXCPrRV6bNZZo9Hw+07dL1k32MmTJwMOfEoH3edT\nIgHFClJAvoemdOd5utrHAnhiJe6pUdgqTH19fQViD9sOfqrRSytaAbnk4L0ozaampryGQOOjugdt\n4MzCwkIQi18ul/39LT2Yah3aB1YSaS4JNYQ333wTQD52lO589ne9613+WSnlqUkNDg4WaOz4LHbc\naadgm4CVMZuZmfF2Bo7Fnj17PGkt3d7j4+MFjTD2TLw/kGspnFt0ofM8dX+v5p5Wmxb7RW0WlPi0\nGQwMDAQu1Hq97q+rmjVx20lWWoHOzk6MjIxgx44dOH78OICVScFQ1MnJSf9SKfU41Su+7AsLC/6F\n4MAq205MRePgqUGQYbf8W5b72rlzp/+NA9vX1+cnp/ImXsvqqyxIWtDDvoScJNpWvjy1Wq1QiZh9\nwHBlJinxfI3I0y0U+02LqtjoP90aWcu+MhrTAMYXcGxszG8L1OquvJsAsHfvXn9/5c4EciIWJiLx\nGleuXCmo9ew/G1vAz2q16vuFfTY2Nua3L3o+t6Mcg3vvvRdAvnDxN47ZwYMHA0ZtJQeK0cRbGvfO\nzs7gGmrkpNeGbe3o6PBFbHULogJKf9Po2bUibR8SEhIKaAtNodFo4NKlS5idnfVqD+nVKAWr1Wrg\nx33Pe97j1Ug1fNFN9eyzzwIo+m75PeaaomTXkmxMyFGuf+vWrFQqgdtRSTksEUetVgu2IB0dHZ5Z\n+Z577gGwUtb85MmTQbx7rVYL6N2cc4Frj8+p8fHKTWhdbzGOST5HtVotSGsg14gYW0IVWtVbSk5N\nB9aUcwB4+eWXsXfvXgAINItdu3b570oLR20xptZbBmQ1GisfJ7eIr7/+uu8rjju3Fpr7wHgJ9qMa\nhy2HZrVaDRL47r77bu8m1YQrSwdIqGamcSp2e9RoNPw8pYalfZI0hYSEhFtCW2gKNIKNjIx4W4KN\n2tq3b5/fV9HosnnzZr8K8vx9+/b5+HLuH7WQqQ380OpBsSAdu9cdHR31q7G6rTTmHSim8hIxklYl\n4yCR6YMPPggAeOyxx3y7rNFSNRElF6UkomuSZLWLi4uBPUDL6KkkZ9+w/2i3mZqaCiS01pPgWFAT\nGRoaCrL2NEqP/XjlyhW88MILAODJVtj+3bt3B8a5rq4ur4FoH1tCXS3Qq/U4tC367Kp9UeNSO4Xa\nXdiP7Aeb1at2EtoiBgcHPc2cumX5rNSOWawYCOekEgKrYdwW+dU+S5pCQkLCLaEtNIVyuYzt27dj\neHjYV1qKWee54h46dAhALrm42mtMPr0DtFprUI11dSpnPveIKsm5elNCzs/P+0AYSsSxsbEgoEnz\nIaxLaHl5OSh42tvb69tNW8ixY8d8H1i33/LycpQjwJZEV/emJYLROHru5ScmJgJtSsO5bY3Fnp6e\ngFyF2k/MbqNtphvx9OnTfgy4/2d7RkdHvWZIr5IGBlGbUW+PrcW5vLzsr6+EJjFq+lhVJ8KSpii1\nOsH2lMtlb5dg+ycmJrw2uHv3bgC5Fmvd5MznaDQaBTIbIJ/T7GelaqOGbTNE1V2+VrTFopA1Kx2/\n/fbbQRFZdsD8/Lx/Qe+77z4AeZEUqlpqdImplkCxkIvGE9gXSdOHOfm0ZN173/teACu1CU6cOBGw\n/9L3DawsTjpQlkm4s7PTu2Nffvll/8xA0eWk24dYJCH7iy+yJjzRcEm1emJiolB8l/1hC+7yk7U5\nFLpg2XwVLVYbS1XXRCGNBOTzEZaopVwuB+3YtGlTwKepjMYUKJqaz3GhofSdd94Jthm6OMQiCW1c\nABeaU6dO+W2sGnaZ/q/bQbbXxsaMjIz49vLY8PCwn3ecH319fThw4ACAlblAwyOF7I0gbR8SEhIK\nWEvZuG8B+HUAF7MsO9A89jUAvwFgEcAbAD6dZdmV5m+PAvgsgCUAX8iy7Nm1NKRUKhWowKy6ubS0\n5FdhGoG2bt3qDTvMqqtWqwFLb4wxOaZZKI++leRc7Xfs2BGkFuv1+KkEJpQ2aty0xCTVatWruPaa\nGlCkUX2aAs378G9ocHzqqaf8c7LfKHG1XoBuB/g9liloaepi0XKqMVitoKOjIwjqUqIWm7J+9erV\ngOW4q6vLS2aOy8DAgNckrZanZf3Uhc35odoD+4ZbGw2SsoFHXV1dgQanz6Tl5glK7p///OcAitsH\nag/UMg8cOOCfme2amZkpcFsC+ZykMV2JX4B8K3KjEY1r0RS+DeAj5tiPABzIsuwggNcBPAoAzrn9\nAD4B4N7m3/yVc+7GKlEkJCTcUaylluR/OOfebY79m/z3CICHmt9/E8B3syyrATjtnDsF4BcBPL/a\nPRiQ0Wg0AgJK3UPTiDM6OgogNzzREKNx7txrcRXn3lHzBTRjkfeiZFebAqWDuhopcRn0ooZJagNn\nzpzxBiZ1J/HT8i9oNSDtFyCX2GqQZHv0d8K6AGmsLJfL/ny2S20bGuBzLdJatR/E6Nisi3RxcXFV\n1mpCw89j5DecE0peats6MTHh9+SWf2F4eNh/V1IeW0FMx93antSGobkYaouxn9YVqP+nxjI2Nhb0\nKV3Ap0+fDrIfN23a5Isk05BZqVQCrU4N2RxvEtNcD7fD0PgZAN9rft+BfJEg3moeWxVMMtIoMA4K\nH6i7u9urUOTsO378uD9f8yLY4VwMbAQYUORvtJNZE0ys8ezNN9/016VaGCvN1Wg0vAHLGqj0RdFo\nREtSopFzvIYWsrW5HZVKJSBGsSnXithipu2NbeVsdKYSzMRK7cUWABunr5PaRm729fVF+88uHs65\nIO1eDXGcR5r6zb5SzxLHltfnliS2VVhcXPQGPS1DaKHtjxG02H7WCFLbf2+//bYvbMPtz/bt2/0C\nYRezBx980C+IJCK6Hm5pUXDOfRlAA8B3buJvPwfgc0DRdZWQkHBncdOLgnPud5AbIB/IVpazCwB2\nyWk7m8cCZFn2OIDHAWBwcDAbGBhArVbzKx2lJVf2ubk574qhhL5w4QL27dsHoFgk1G4RtPimXY0V\nKgGuFeWo5es0ws66sLQ2gXXLaUYkV3gttEL1l33w6quveu2Iv/X29noJx8/+/v6AWzD27OqqY3up\nSehWxWoAqubHCunadGaN06dmppoCny8WKxIrIhOTwoS6lm0sSr1e99ePXYP9snPnTk/5F4tuJTS/\ngdejpqAZo+wPy/6t99TjqxkEdVtCDZRazfnz5wOuTcZ0bN68uVALZS24KZekc+4jAP4QwMezLFOT\n+dMAPuGc63bO7QGwF8B/xq6RkJDQnliLS/JJAB8EMOKcewvAV5B7G7oB/Ki5HzqSZdnvZll21Dn3\nfQDHkG8rPp9l2XWpZEmy4pwrGFT4G5Abd2hoZDbh2bNn/fkaWabEIvr/64FSamFhwUsA+7cx6Zpl\nmZcUSobC37kvpZQfHh4O7Adq5KJUo3RVwg6e09/fH7jZqtWq15h4PoOY9Dk0kIiSRffmVsNRG4N1\nHSp5C9uhxVAtM/Xy8nJQ7UrHnZ8aNBSL3Y8xXttjauOw2try8nKQg7Fp0yY/ZrwGtdLx8fGAw6Gr\nq8u7M5lByX5ZWFjwf0tJPTMzE2RCxqINVXOIRVbGXOx2XKhVnz179oZL0a/F+/Bw5PA3Vzn/qwC+\neiONYESjGvj0NyBXlzkoVJdHRkb8YNOQdOrUqaDOIF8u9bNrh9oXQwfAFkRRtZZtrVarQZxCZ2dn\nkAqtE06TcHjMTlx9yThx1Uug4bNAvjDa6Ex9sWMs0ZYcpl6vB2rsanUJdRGxvJZ6LU3ltklYeg8N\nqQbyMbY2J+0/NXjaLZ8uOrrt4jH2B4XBG2+8UVjQ9HPz5s1BdejZ2dlCYpi2p1wuexWenwsLC35u\ncqGYnZ0Nthc6PnYBUOOwLu7X2lqpIX2tSBGNCQkJBbRF7kOpVMLg4CBqtZpfebUIB8/hqk3JOD8/\n73/X5CdrVFJ12PIU6nXVWGPVWJUc1oBUq9UKhCs8z24fVApayjBlLaZUoEHp0qVLXkPRtFnei1pM\nvV4PVEWVJvZYTCWlxqb3ihnCNI7D/h6TWjFKN+Ly5cvXLJmn2o+Oo9UKGo1GwCXJrVl3d3e0Cjel\ntUpq/i37VlmxLYdiuVz285Vjp0lT2jb+Ha/BubF169bA7c35OzMzE2g9qinoXLBjoNrGagbaGJKm\nkJCQUEBbaAosRb+4uBjshTWQg/tCGhxnZmbw/PPP++8AcP/99/vzKJlVMloXmab+cvXWuHWu2lrK\nyxrgYhWlqtWqJzDlPpL3XFxc9FKB2sDU1FRgrCQ0SpFaR0dHh38+HhsbGwvsErqfXE1DUJdaTLsg\nYte3ZLiavss+4jlbtmzxthDi6NGjQXSmuk9tQJYyIMdK4FloPoxew2ZmasQrn5Na6djYmDdMsr9H\nRkYCNmfmTMzPzxeo4oB8/FV7YTus5Oecu3z5so9u1DTvWN0RCx3rWHn61ZA0hYSEhALaQlNYXl72\n+e2UfpaifGpqKqBq6+rqCuoenjt3LqAH08AiG0cfW6k1M89ahPk3+tnf3+9XftVwbGUjrXVIzYZ7\nzOHh4UKgEbBioXbO+ed74IEHAORx8czXpwTr7u4OJLla6VerVKTSxFr2VXrHqMQtXZpmUFKDIuHJ\n0NBQgXqd14iRkQJFqjtqGKoVaMASr8c+UDcs+5lagfaBBqbZACgNUGObqN1NT08HFcf4vMPDw1EC\nIJsHox4xdYmzjXR56lym5kmtRJ/Tjv/NoG0WhatXrxaMVuSy44uRZZnnGyRJyOTkpJ8wXDCAlZeE\nEYLsPHWnaZy8NVoBxQg8hRrzqDoODAwUUn2B/KXhPY4ePVq4p3POq5G8Rk9PT5Auzsmn/IAsqnL2\n7FnfNq1+TVgDqfL/xyaMGqrsSx6Ll9DzrZErdn327dTUVBABqXEEvCeFwtDQUMA7GNtmKlekTavW\nZCZ9GWM1GKxbVRcJLaLDe/Jvbfr90NCQX8Q4xktLS34+MzlpZmYmUO85f2NUAvV6Pdiqzs3NBSxZ\nsViHtSJtHxISEgpwt6Jm3LZGODcJYB7Apeud2wKMILVDkdpRxP/mduzOsmzz9U5qi0UBAJxzP8uy\n7H2pHakdqR13th1p+5CQkFBAWhQSEhIKaKdF4fE73YAmUjuKSO0o4v98O9rGppCQkNAeaCdNISEh\noQ3QFouCc+4jzrlR59wp59yXWnTPXc65nzjnjjnnjjrnvtg8Puyc+5Fz7mTzc6hF7Sk5515yzj3T\n/P8e59wLzT75nnOufL1r3IY2DDrnfuCcO+GcO+6ce/+d6A/n3O83x+Q159yTzrmeVvWHc+5bzrmL\nzrnX5Fi0D1yOv2y26RXn3KF1bsfXmmPzinPuH51zg/Lbo812jDrnPnwr977ji4LL60I8BuCjAPYD\neNjl9SPWGw0Af5Bl2X4AhwF8vnnfLwH4cZZlewH8uPn/VuCLAI7L//8EwJ9nWXY3gMvIC+ysN74O\n4F+zLNsH4Bea7WlpfzjndgD4AoD3ZXnxoRLyWiKt6o9vI6xzcq0++ChyysG9yEmIv7HO7WhNvRXm\nZ9+pfwDeD+BZ+f+jAB69A+34IYBfAzAKYFvz2DYAoy24907kk+1XADwDwCEPTOmM9dE6tWEjgNNo\n2pnkeEv7A3lJgPMAhpGH4T8D4MOt7A8A7wbw2vX6AMDfAHg4dt56tMP89lsAvtP8XnhnADwL4P03\ne987rilgZRIQa6oVcTvh8mI39wN4AcCWLMvGmj+NA9jSgib8BXIiXCZnbAJwJcsyBsW3ok/2AJgE\n8ERzG/O3zrk+tLg/siy7AOBPAZwDMAZgGsCLaH1/KK7VB3dy7n4GwL+sRzvaYVG4o3DObQDwDwB+\nL8uyGf0ty5fddXXPOOdYp/PF9bzPGtAJ4BCAb2RZdj/ysPPCVqFF/TGEvNLYHgDbAfQhVKPvGFrR\nB9eDu4V6K2tBOywKa64VcbvhnOtCviB8J8uyp5qHJ5xz25q/bwNwcZ2b8UsAPu6cOwPgu8i3EF8H\nMOicYxZrK/rkLQBvZVn2QvP/P0C+SLS6P34VwOksyyazLKsDeAp5H7W6PxTX6oOWz123Um/lk80F\n6ra3ox0Whf8CsLdpXS4jN5g8vd43dXlO6jcBHM+y7M/kp6cBPNL8/ghyW8O6IcuyR7Ms25ll2buR\nP/u/Z1n2SQA/wUqNzla0YxzAeefcPc1DDyCn6m9pfyDfNhx2zvU2x4jtaGl/GFyrD54G8KmmF+Iw\ngGnZZtx2uFbVW1lPo9ENGFQ+htya+gaAL7fonr+MXA18BcB/N/99DPl+/scATgJ4DsCjQg5MAAAA\npUlEQVRwC/vhgwCeaX6/qzmwpwD8PYDuFtz/PgA/a/bJPwEYuhP9AeCPAJwA8BqAv0NeY6Ql/QHg\nSeS2jDpy7emz1+oD5Abhx5rz9lXkHpP1bMcp5LYDzte/lvO/3GzHKICP3sq9U0RjQkJCAe2wfUhI\nSGgjpEUhISGhgLQoJCQkFJAWhYSEhALSopCQkFBAWhQSEhIKSItCQkJCAWlRSEhIKOB/AHxSHlK2\n6g0ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa5b6ca0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img[1].squeeze(), cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 24   4  26  16  -9 -17  20  20]\n"
     ]
    }
   ],
   "source": [
    "# The order (although it doesn't matter) is: top-left, bottom-left, bottom-right, top-right\n",
    "print(offsets[sample_idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Efficient loading in Keras using a Python generator\n",
    "\n",
    "import os.path\n",
    "import glob\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "def data_loader(path, batch_size=64):\n",
    "    \"\"\"Generator to be used with model.fit_generator()\"\"\"\n",
    "    while True:\n",
    "        for npz in glob.glob(os.path.join(path, '*.npz')):\n",
    "            # Load pack into memory\n",
    "            archive = np.load(npz)\n",
    "            images = archive['images']\n",
    "            offsets = archive['offsets']\n",
    "            # Yield minibatch\n",
    "            for i in range(0, len(offsets), batch_size):\n",
    "                end_i = i + batch_size\n",
    "                try:\n",
    "                    batch_images = images[i:end_i]\n",
    "                    batch_offsets = offsets[i:end_i]\n",
    "                except IndexError:\n",
    "                    continue\n",
    "                # Normalize\n",
    "                batch_images = (batch_images - 127.5) / 127.5\n",
    "                batch_offsets = batch_offsets / 32.\n",
    "                yield batch_images, batch_offsets\n",
    "\n",
    "# Dataset-specific\n",
    "train_data_path = '/path/to/training-data'\n",
    "test_data_path = '/path/to/test-data'\n",
    "num_samples = 150 * 3072 # 158 archives x 3,072 samples per archive, but use just 150 and save the 8 for testing\n",
    "\n",
    "# From the paper\n",
    "batch_size = 64\n",
    "total_iterations = 90000\n",
    "\n",
    "steps_per_epoch = num_samples / batch_size # As stated in Keras docs\n",
    "epochs = int(total_iterations / steps_per_epoch)\n",
    "\n",
    "# model is some Keras Model instance\n",
    "\n",
    "# Train\n",
    "model.fit_generator(data_loader(train_data_path, batch_size),\n",
    "                    steps_per_epoch=steps_per_epoch,\n",
    "                    epochs=epochs)\n",
    "\n",
    "# Test\n",
    "model.evaluate_generator(data_loader(test_data_path, 1),\n",
    "                         steps=100)"
   ]
  },
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   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  }
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